Update app.py

app.py

@@ -1,71 +1,71 @@
-import torch
-from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
-import gradio as gr
-from PIL import Image
+# import torch
+# from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
+# import gradio as gr
+# from PIL import Image
 
-# Use a publicly available high-capacity model.
-# For instance, we use "google/pix2struct-docvqa-large". 
-# (If you need a different model or a private one, adjust accordingly and add authentication if necessary.)
-model_name = "google/pix2struct-docvqa-large"
+# # Use a publicly available high-capacity model.
+# # For instance, we use "google/pix2struct-docvqa-large". 
+# # (If you need a different model or a private one, adjust accordingly and add authentication if necessary.)
+# model_name = "google/pix2struct-docvqa-large"
 
-model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
-processor = Pix2StructProcessor.from_pretrained(model_name)
+# model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
+# processor = Pix2StructProcessor.from_pretrained(model_name)
 
-def solve_problem(image):
-    try:
-        # Ensure the image is in RGB.
-        image = image.convert("RGB")
+# def solve_problem(image):
+#     try:
+#         # Ensure the image is in RGB.
+#         image = image.convert("RGB")
         
-        # Preprocess image and text prompt.
-        inputs = processor(
-            images=[image],
-            text="Solve the following problem:",
-            return_tensors="pt",
-            max_patches=2048
-        )
+#         # Preprocess image and text prompt.
+#         inputs = processor(
+#             images=[image],
+#             text="Solve the following problem:",
+#             return_tensors="pt",
+#             max_patches=2048
+#         )
         
-        # Generate prediction.
-        predictions = model.generate(
-            **inputs,
-            max_new_tokens=200,
-            early_stopping=True,
-            num_beams=4,
-            temperature=0.2
-        )
+#         # Generate prediction.
+#         predictions = model.generate(
+#             **inputs,
+#             max_new_tokens=200,
+#             early_stopping=True,
+#             num_beams=4,
+#             temperature=0.2
+#         )
         
-        # Decode the prompt (input IDs) and the generated output.
-        problem_text = processor.decode(
-            inputs["input_ids"][0],
-            skip_special_tokens=True,
-            clean_up_tokenization_spaces=True
-        )
-        solution = processor.decode(
-            predictions[0],
-            skip_special_tokens=True,
-            clean_up_tokenization_spaces=True
-        )
-        return f"Problem: {problem_text}\nSolution: {solution}"
-    except Exception as e:
-        return f"Error processing image: {str(e)}"
+#         # Decode the prompt (input IDs) and the generated output.
+#         problem_text = processor.decode(
+#             inputs["input_ids"][0],
+#             skip_special_tokens=True,
+#             clean_up_tokenization_spaces=True
+#         )
+#         solution = processor.decode(
+#             predictions[0],
+#             skip_special_tokens=True,
+#             clean_up_tokenization_spaces=True
+#         )
+#         return f"Problem: {problem_text}\nSolution: {solution}"
+#     except Exception as e:
+#         return f"Error processing image: {str(e)}"
 
-# Set up the Gradio interface.
-iface = gr.Interface(
-    fn=solve_problem,
-    inputs=gr.Image(type="pil", label="Upload Your Problem Image", image_mode="RGB"),
-    outputs=gr.Textbox(label="Solution", show_copy_button=True),
-    title="Problem Solver with Pix2Struct",
-    description=(
-        "Upload an image (for example, a handwritten math or logic problem) "
-        "and get a solution generated by a high-capacity Pix2Struct model.\n\n"
-        "Note: For best results on domain-specific tasks, consider fine-tuning on your own dataset."
-    ),
-    examples=[
-        ["example_problem1.png"],
-        ["example_problem2.jpg"]
-    ],
-    theme="soft",
-    allow_flagging="never"
-)
+# # Set up the Gradio interface.
+# iface = gr.Interface(
+#     fn=solve_problem,
+#     inputs=gr.Image(type="pil", label="Upload Your Problem Image", image_mode="RGB"),
+#     outputs=gr.Textbox(label="Solution", show_copy_button=True),
+#     title="Problem Solver with Pix2Struct",
+#     description=(
+#         "Upload an image (for example, a handwritten math or logic problem) "
+#         "and get a solution generated by a high-capacity Pix2Struct model.\n\n"
+#         "Note: For best results on domain-specific tasks, consider fine-tuning on your own dataset."
+#     ),
+#     examples=[
+#         ["example_problem1.png"],
+#         ["example_problem2.jpg"]
+#     ],
+#     theme="soft",
+#     allow_flagging="never"
+# )
 
-if __name__ == "__main__":
-    iface.launch()
+# if __name__ == "__main__":
+#     iface.launch()